In a previous post, we talked about how to perform top cut analysis in Surpac by using the mean and variance top cut diagram inside the Basic Statistics window. The diagram plots the mean against the Coefficient of Variation (COV) at various grade cut offs, allowing the geologist to assess which cut off will yield an acceptable COV and its impact on the mean grade.
Today, we will talk about some alternative and complementary methods documented across the industry for helping geologists select and decide on the appropriate top cut value, such as percentiles, Sichel’s mean, log-probability plots – all of which can be performed in Surpac.
Percentiles:
In the Basic Statistics Window data can be reported by going to Statistics > Report.
The above form shown provides the user with the capability to edit the reported percentile ranges. In this example, the default range has been edited to include the 98th and 99th percentiles. To generate the report, hit apply. The reported percentile grade values could then be considered/assessed for the top cut value.
Log-probability plots:
A Log-probability plot can be generated inside the Basic Statistics Window following loading of data from a string file, as per above.
Once you have done this, apply a log transformation to the data by going to Statistics > Transformations.
Next display the Log-Probability Plot: Display > Probability Curve. The inflection points in this curve may indicate the separation between different sample populations and/or high/low tails. For example the last inflection point could be used as a suitable value for top cutting the dataset.
How do I apply a top cut on a string file?
This is typically done directly on the string file, using the string math functionality. An example of a top cut expression is provided below:
In this example the d1 field contains the uncut dataset and the d2 field will store the data top cut at 24.6. This string math technique allows different top cuts to be stored in different d-fields for subsequent analysis in the Basic Statistics Window. This makes it easy to compare different top cuts at the same time and assess the key statistics.
Grade Dependent Search Ellipses:
Lastly it is important to note other methods exist across the industry for controlling the influence of high grade samples in a population. In Surpac 6.7, we added the option to create grade dependent search ellipses. This new tab appears in either the Inverse Distance or Ordinary Kriging Search Parameters form and gives the geologist the ability to specify a number of anisotropic search distances for specified grade ranges.
This means you can restrict the influence of the samples selected in a manner that is sensitive to the magnitude of the sample grade, which can help you to better estimate deposits with multiple populations of mineralization existing within a single estimation domain.
If you missed our previous posts, check out Top Cut Analysis in GEOVIA Surpac 6.7.4 or How to perform Dynamic Anisotropy in Surpac.
